Provides the extended ClopperPearson confidence limits for a failure model, where countermeasures are introduced.
1 2  cm.clopper.pearson.ci(n, size, cm.effect, alpha = 0.1, CI = "upper", uniroot.lower = 0,
uniroot.upper = 1, uniroot.maxiter = 1e+05, uniroot.tol = 1e10)

n 
sample size. 
size 
vector of the number of failures for each type. 
cm.effect 
vector of the success probabilities to solve a failure for each type. Corresponds to the probabilities pi of a generalized binomial distribution. 
alpha 
significance level for the (1alpha)* 100% confidence level (default alpha=0.1). 
CI 
indicates the kind of the confidence interval, options: "upper" (default), "lower", "two.sided". 
uniroot.lower 
The value of the 
uniroot.upper 
The value of the 
uniroot.maxiter 
The value of the 
uniroot.tol 
The value of the 
This is an extension of the ClopperPearson confidence interval, where different outcome scenarios of the random sampling are weighted by generalized binomial probabilities. The weights are the probabilities for observing 0,...,k failures after the introduction of countermeasures. Computes the confidence limits for the p of a binomial distribution, where p is the failure probability. The failures are tackled by countermeasures for specific failure types with different effectivity. See the references for further information.
A data frame containing the kind of the confidence interval, upper and lower limits and the used significance level alpha
.
D.Kurz, H.Lewitschnig, J.Pilz, DecisionTheoretical Model for Failures which are Tackled by Countermeasures, IEEE Transactions on Reliability, Vol. 63, No. 2, June 2014.
uniroot
, dgbinom
, clopper.pearson.ci
1 2 3 4 5 6 7  ## n=110000 tested devices, 2 failures divided in 2 failure types k1=1, k2=1.
## 2 countermeasures with effectivities p1=0.5, p2=0.8
cm.clopper.pearson.ci(110000,size=c(1,1),cm.effect=c(0.5,0.8))
# Confidence.Interval = upper
# Lower.limit = 0
# Upper.limit = 3.32087e05
# alpha = 0.1

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